aithy: Skill for Claude Code

Playbook Codegen Rules (@ax-llm/ax)

Use this skill to generate context-playbook code. A playbook grows an evolving body of task knowledge and renders it into a program's context. The evolution engine (ACE — Agentic Context Engineering) is hidden behind playbook(...), exactly as optimize(...) hides its optimizer. Prefer the playbook(...) concept; only reach for AxACE directly when the user explicitly wants the low-level engine.

Use These Defaults

• Create with playbook(program, { studentAI, teacherAI? }); it returns an AxPlaybook handle.
• Grow offline with await pb.evolve(examples, metric) — returns { bestScore, playbook }.
• Grow online with await pb.update({ example, prediction, feedback }) — no metric needed.
• Apply with pb.applyTo(program) (defaults to the bound program).
• Persist with pb.toJSON() and restore with playbook(program, opts).load(snapshot).
• Inspect with pb.render() (markdown) and pb.getState() ({ playbook, artifact }).
• For agents use agent.playbook({ target: 'actor' | 'responder' }); default target is 'actor'.
• Use a cheaper studentAI to run the program and an optional stronger teacherAI to reflect/curate.
• Prefer ai(), ax(), and agent() for new code.

Critical Rules

• playbook(...) binds to an AxGen program; evolve/update need that program's signature.
• evolve() returns only { bestScore, playbook }. There is no Pareto front and no optimizedProgram — that is optimize(...)'s shape, not a playbook's.
• update({ example, prediction, feedback }) requires the full { example, prediction }; example must match the program's input fields (plus any expected output). Do not pass bare input fields at the top level.
• update() works without a prior evolve()/load() — the handle hydrates lazily on first use.
• applyTo() injects a ## Context Playbook block into the program description; calling it repeatedly recomposes from the original base (no stacking).
• Keep the offline metric deterministic and cheap, like a GEPA metric.
• A playbook is plain JSON. Persist pb.toJSON() and load(...) it into a fresh program for production.
• The playbook engine, construction-time agent attachment, failure harvesting, and verified agent evolution are available in TypeScript and the generated Python, Java, C++, Go, and Rust packages. Use each package's native casing and callback types.